‘I’m Not a Tenant They Can Just Run Over’: Low-Income Renters’ Experiences of and Resistance to Racialized Dispossessing
Bibliographic record
Abstract
Racialized housing markets are a cornerstone of systemic racial inequality in the United States, affecting socioeconomic, wealth, health, and educational outcomes. To enrich critical sociological research on housing, we examine how low-income renters perceive, experience, and navigate racialized dispossessing , or the everyday processes by which people of color are severed from place, home, and stability in rental markets. Drawing on in-depth interviews with 43 low-income American Indian, Black, Latinx, and White renters across two research sites, we find that low-income renters of color routinely experience other-race landlord and property manager non-responsiveness to housing quality and safety issues while White renters experience responsiveness. We also show how renters of color perceive and experience landlords and property managers racializing them as inferior, at times to justify this dispossession. In contrast to most of their counterparts of color, we demonstrate how low-income American Indian renters in our sample with same-Tribe landlords or property managers are protected from the harms their counterparts face. Finally, we show how low-income renters of color use a variety of strategies to resist this racialized dispossessing, often at great emotional or financial cost. We conclude by discussing the implications of our findings for research and housing policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".